Abstract: In order to realize the high-precision application of digital images and reduce the influence of external light on visual imaging, a fast blind restoration algorithm for visual defocused light images based on variational Bayesian is proposed. Through gradient and convolution processing, the posterior probability expectation of the visual defocused light image is calculated, and the optimal initial image and the prior probability of the defocused light blur function are extracted by using the Sobolev space function distribution method. The actual posterior probability is reached infinitely, using relative entropy to calculate the distance between multiple distributions, to approximate the true value of the greatest extent, and input the minimum loss cost function into the bilateral filter, that is, take the approximate clear image as the guide map, to remove the remaining high-frequency noise. The optimal image blind restoration results are obtained. The experimental results show that the proposed algorithm has high image contrast, clear edge details and fast restoration speed after blind restoration, which has extremely high application value.

Key words: minimum loss cost function,  , variable decibels,  , defocus image,  , a posteriori probability,  , relative entropy,  , bilateral filter